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Category: Machine Learning

Last week I presented this topic to professional women at PWI here in Brussels. It’s called ‘small talk’ because it is not a technical presentation but one for a broader audience, to create awareness on this Big Data trend. The main concept I wanted them to take away is the change in the business arena and in our society due to Big Data. If you are interested on this subject, just drop a line and let me know!

Prices of discs and storage devices have dropped a lot, so now basically any digital data is being stored. Cost is so low, that it is worth to save it ‘just in case, and we’ll see in the future what we can do with this data’. Technology has made also huge advances with massive parallel processing, and we can manage to jungle through thousands of servers to analyse a bunch of diverse data and extract information from it in a usable time-frame.

This allows business strategists to make smarter decisions based on facts, better than how it was done before, based on experience or intuition. So the message for all decision-makers is: go and check your data, you’ll find there valuable information to decide any business matter. Also, be aware that your competition is going into it too, it can out-smart you!

At the society level, there are many ethical issues to deal with, like privacy or equality and fairness. What to you think, is it fair to have a subsidy that is ‘personalised’, that may give more to someone than to others because of a particular factor, or allow access to a health treatment to someone and not to another based on his life expectancy for example? What about basing the decision on his ‘ROI’ like the capability of paying back for the given treatment? Or is it more fair to have instead equality on subsidies, same amount for everyone? Even for the ones that could pay it by themselves? Either we discuss them before-hand, or we will be at the mercy of any politician or entrepreneur taking a step deeper in an unethical direction.

And as a last twist, I would like to point out that thebasic value of knowledge is challenged. We are already experiencing a change of values, knowledge is less and less valued as an asset anymore, but value remains in knowing how to get to the knowledge,where to find it and what to extract from data.

Andyvision is the name of this ET-looking robot. You can find it at the CMU store near the Carnegie Mellon University, checking the inventory.

Andyvision[…] scans the shelves to generate a real-time interactive map of the store, which customers can browse via an in-store screen. At the same time, the robot performs a detailed inventory check, identifying each item on the shelves, and alerting employees if stock is low or if an item has been misplaced.

While making its rounds, the robot uses a combination of image-processing and machine-learning algorithms; a database of 3-D and 2-D images showing the store’s stock; and a basic map of the store’s layout—for example, where the T-shirts are stacked, and where the mugs live. The robot has proximity sensors so that it doesn’t run into anything.

The map generated by the robot is sent to a large touch-screen system in the store and a real-time inventory list is sent to iPad-carrying staff.

This is not a break-through discovery, there is nothing technologically new. It is a great example of innovation, of what can be done by just combining existing types of algorithms in a novel way. It is based on many computer-vision programs, as scanning barcodes, reading text, and using visual information of shape, size or color to identify an item. But it can also infer the identity from the knowledge it has of the structure of the shop and its proximity to other items:

“If an unidentified bright orange box is near Clorox bleach, it will infer that the box is Tide detergent,” she says.

Narasimhan’s group developed the system after interviewing retailers about their needs. Stores lose money when they run low on a popular item, and when a customer puts down a jar of salsa in the detergent aisle where it won’t be found by someone who wants to buy it; or when customers ask where something is and clerks don’t know. So far, the robotic inventory system seems to have helped increase the staff’s knowledge of where everything is. By the fall, Narasimhan expects to learn whether it has also saved the store money.

Narasimhan thinks computer-vision inventory systems will be easier to implement than wireless RFID tags, which don’t work well in stores with metal shelves and need to be affixed to every single item, often by hand. A computer vision system doesn’t need to be carried on a robot; the same job could be done by cameras mounted in each aisle of a store. [..] The biggest challenge for such a system, she says, is whether it “can deal with different illuminations and adapt to different environments.”

After its initial test at the campus store, Narasimhan says, the Carnegie Mellon system will be put to this test in several local stores sometime next year.

I particularly find it cute to have an ET wandering around, so let’s hope their economical expectations are fulfilled, and think of for more innovative ideas of this order!

UCLA neuroscientist researchers just released the promising results of a study, where they measured the prediction of machine learning algorithms on ‘brain reading’ (also called ‘brain decoding’). The research was funded by the National Institute on Drug Abuse, and lead by Dr. Ariana Anderson. They used cigarette smokers, showing to some of them videos that will make them feel the crave for nicotine, in which case they where instructed to fight their addiction. They scanned their brains during the process, in order to capture their mental active zones, and used that information as input for the machine learning programs. “We detected whether people were watching and resisting cravings, indulging in them, or watching videos that were unrelated to smoking or cravings,” said Anderson, the used ML methods could infer up to 90% of accuracy, if the person has been put in a craving situation or not, and even if it has been fighting it. They could anticipate and predict their future mental state, in a similar way as the text-entry tools on cell phones do, predicting your next word based on the first letters. The functional RMIs also shown the regions of the brain that were used to fight the nicotine addiction, what they expect to be of great help to control any other drug cravings.

Yesterday was the great day. Eleven hours of TED, full of talks of around 8 minutes each: what a challenge for the speakers, and for the audience! It was tiring, but worth it. What did I like the most? By far and on a different register than the other speakers, Paddy Ashdown: what a clear picture of globalisation, the playing forces and the need for governance. So good I’m happy we will have it on video to hear it again, and pass it on to my friends.

Now I hope you will excuse me if my AI background makes me mention more in detail the talks about Robotics 🙂 I was impressed by the Geminoid. They had some little problems for the demo, but the ressemblance and his facial expressions seemed very real. How human must the gemanoid look that on my sentence I said ‘his face’ and not ‘its face’ 🙂 I thought about it, but it didn’t feel wrong. On the same category, Luc Steels presented an altogether different aspect of the robots: not in the human look-like, but on the learning behaviour: they initialise a ‘mental state’ for robots that can be downloaded on a Sony robotic harwdare. Each mental state evolves through the interaction with another robot, trying to communicate, creating and learning words that represent the objects of their world. I see the robots going through our evolutionary steps, at a drastically different pace than us. It is not ‘if’ anymore but ‘when’ : When will be the moment we will consider them sentient? How society will react to that?

I don’t want to end without mentioning it. We even had a ballet of electrons on scene. Grandiose! Physics explained through danse. Matter, laser movements, quantum mechanics, the flow… I really encourage all science teachers to use this video in their class, to explain those concepts to the kids. So easy to understand, so visual!

For the other talks, very interesting too, you will have to come back, that’s all for this post.

This API allows you to create a Classifier, that learns from a dataset of examples. Your example data can be numeric or plain text, and it results in hundreds of categories. It has built-in several machine learning algorithms, and it chooses automatically one for your needs.

As Travis Green explains it:

Now your apps can get smarter with as little as a single line of code. They can learn to continually adapt to changing conditions and to integrate new information. This week at Google I/O, we’re making the Google Prediction API generally available, meaning you can create apps with these capabilities for yourself. Additionally, we’re introducing several significant new features, including:

The ability to stream data and tune your predictive models

A forthcoming gallery of user-developed, pre-built models to add smarts even faster

So not only you can create your own Classifier, but you can just use one ‘Ready to use’, made by a Third Party. I’m pretty sure there are millions of applications for which it will be great to reuse an already trained program.

Just one caveat, one of the major critics of some algorithms in Machine Learning is that the classification has no explanation. So getting somebody else’s Classificator feels like trusting somebody else’s criteria. No problem having a classifier to predict what film I will like, but at the pace technology is evolving and invading our everyday life, soon we will be using it for bigger decision makings. So let’s improve those algorithms to get rid of all biases!

Have you read the Robots Series from Isaac Asimov? This article by G. Ananthakrishnan reminded me of Solaria, the planet where its inhabitants had no contact with each other, too afraid of getting contaminated by microbes. They visited each other through sophisticated holographic viewing systems instead . Read about next product of Microsoft Research, being created by 850 PhDs mainly in the field of machine learning, called “Avatar Kinect”, and tell me what do you think about it. Will our future reserve us also a strong phobia towards actual contact?

Kinect can see and hear

Kinect, which was launched on November 4 last year, has sold 10 million units and entered the Guinness Book as the fastest selling consumer electronics device in history, bar none. It features instant streaming of high definition 1080p content, reads body and facial gestures, and responds to voice commands. Adding to the existing feature set, “Avatar Kinect” will allow X Box Live users to chat and interact online socially in their ‘avatar’ (a faithful and live animation character of themselves) starting in the first half of 2011. This forms part of Microsoft’s approach to more closely integrate socialisation features into its products.

The Kinect console uses cutting-edge technology to read the movements of the person in front of it, even to the point of reproducing smiles, frowns and raised eyebrows and other facial expressions. So how does it do this?

The gadget uses its own light source to illuminate the room, whether it is pitch dark or brightly illuminated, to ‘understand’ the surroundings. Alex Kipman, the Director of Incubation, says this technology enables one of the ‘eyes’ of the Kinect to see the room, as a monochrome view. “Things that are super close to the sensor are white, super far away are black, we file both of those numbers away and focus on the infinite shades of grey in between. For each shade of grey it maps a real-world coordinate, the distance, eyeball, a point. A colour eye, as in a phone or camcorder allows us to capture the user’s memories, and enable video conferencing. It also recognises when you are walking towards the sensor,” Mr. Kipman says.

The ‘ears’ of the device sit underneath the sensor, and they are essentially four microphones in an asymmetrical configuration. This acoustic chamber is a first, a system created with a non push-to-talk feature. The environment is always-on and listening. So, in the living room when people are having fun creating a lot of ambient sounds, the sensor is still able to differentiate the speech of different individuals through robust voice recognition.

Color is a new photo-sharing application for iPhone and Android phones. It allows you to share the photos you have taken through it with any other Color user near you. And you get to see also his or her pictures 🙂 They defined a ‘proximity’ criteria, that creates local temporary networks. As soon as you leave the place, if you are no longer close to the other user, you loose access to his photos (and he to yours).

But if you hang around the same Color user for some time, photos will remain longer than with an occasional user. Color is using machine learning algorithms to create this ‘elasticity’. Great concept!

Much of last week’s buzz surrounding the launch of Color was justifiably skeptical. The startup, after all, raised $41 million to enter a crowded space without a business model or customers, and many wonder whether the world really needs another mobile photo-sharing app. But two components of Color’s vision — implicit networks (connections created without user effort) and place/time tagging — extend far beyond photo-sharing, and make the company worth watching as a potential indicator of social media and data-mining trends.